A Bayesian Hierarchical Model for US Election Data

MCMC diagnostic plots

Abstract

We compare federal election results for each state versus the USA in every second year from 1992 to 2016 to model partisan lean of each state and its dependence on the nationwide popular vote. For each state, we model both its current partisan lean and its rate of change, as well as sensitivity of state results with respect to the nationwide popular vote, using a Bayesian Hierarchical Model. We then apply this model to predict and compare results with the actual values for the 2018 election.

Type
Benjamin Osafo Agyare
Benjamin Osafo Agyare
PhD Student in Statistics